Lean mass reference curves in adolescents using dual-energy x-ray absorptiometry (DXA)

Ripka, Wagner Luis; Orsso, Camila E.; Haqq, Andrea M.; Luz, Thais Gretis; Prado, Carla M.; Ulbricht, Leandra

doi:10.1371/journal.pone.0228646

Cited by 12 publications

(15 citation statements)

References 32 publications

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“…Body composition and systolic blood pressure differed significantly by sex but were largely in line with previous findings in adolescence [47][48][49]. Considering these differences, we performed additional sex-specific regression analyses for systolic blood pressure, waist circumference, and total body and trunk fat percentage.…”

Section: Plos Onesupporting

confidence: 76%

Less physical activity and more varied and disrupted sleep is associated with a less favorable metabolic profile in adolescents

et al. 2020

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Background Sleep and physical activity are modifiable behaviors that play an important role in preventing overweight, obesity, and metabolic health problems. Studies of the association between concurrent objective measures of sleep, physical activity, and metabolic risk factors among adolescents are limited. Objective The aim of the study was to examine the association between metabolic risk factors and objectively measured school day physical activity and sleep duration, quality, onset, and variability in adolescents. Materials and methods We measured one school week of free-living sleep and physical activity with wrist actigraphy in 252 adolescents (146 girls), aged 15.8±0.3 years. Metabolic risk factors included body mass index, waist circumference, total body and trunk fat percentage, resting blood pressure, and fasting glucose and insulin levels. Multiple linear regression adjusted for sex, parental education, and day length was used to assess associations between metabolic risk factors and sleep and activity parameters. Results On average, participants went to bed at 00:22±0.88 hours and slept 6.2±0.7 hours/night, with 0.83±0.36 hours of awakenings/night. However, night-tonight variability in sleep duration was considerable (mean ± interquartile range) 0.75±0.55 hours) and bedtime (0.64 ±0.53 hours) respectively. Neither average sleep duration nor mean bedtime was associated with any metabolic risk factors. However, greater night-tonight variability in sleep

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Section: Plos Onesupporting

confidence: 76%

Less physical activity and more varied and disrupted sleep is associated with a less favorable metabolic profile in adolescents

et al. 2020

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“…Analyses of multiple exposures could have increased type I error. Body composition measures differ by sex in adolescence 54 ; although interactions by sex were not statistically significant, effect modification by sex cannot be completely ruled out. The lack of available body composition measurements in infancy prevented us from analyzing change in these outcomes or adjusting for their baseline values.…”

Section: Discussionmentioning

confidence: 71%

Serum polyunsaturated fatty acids in infancy are associated with body composition in adolescence

et al. 2020

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Background: Polyunsaturated fatty acids (PUFA) have been related to the development of adiposity. N-3 PUFA appears to be protective against obesity risk, while n-6 PUFA may be associated with greater adiposity. However, most studies have been conducted among adults. The role of PUFA in infancy is unknown. Objective: To examine associations of serum PUFA at age 1 year with age-and sexadjusted body mass index Z score (BMIZ) change through age 16 years and body composition at 16 years. Methods: We quantified serum PUFA in 636 Chilean infants aged 1 year. We measured BMIZ at ages 1, 5, 10 and 16 years, and body composition by dual energy X-ray absorptiometry at 16 years. We estimated differences in 1-to 16-years BMIZ change between PUFA quartiles from multivariable linear mixed models with restricted cubic splines. At 16 years, we estimated differences in total fat mass (ToFM), truncal fat mass (TrFM), total lean mass (TLM), percent total fat mass (%ToFM) and percent truncal fat mass (%TrFM) between PUFA quartiles using linear regression. Results: PUFA were not associated with BMIZ change. Alpha-linolenic acid (ALA) was positively associated with TrFM (P = .03) and %TrFM (P < .0001) at 16 years while eicosapentaenoic acid (EPA) was inversely associated with %TrFM (P = .001). Docosapentaenoic acid (DPA) was positively associated with ToFM (P = .01), TrFM (P = .009), %ToFM (P = .02) and %TrFM (P = .02). Gamma-linolenic acid (GLA) and the Δ6-desaturase (D6D) activity index were each positively, linearly associated with ToFM, TrFM and %ToFM. The Δ5-desaturase (D5D) activity index was inversely associated with %TrFM (P = .04).

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“…As children go through puberty, there are significant effects on body composition. Girls acquire a greater amount of fat mass, and boys gain significantly more fat-free mass, and both genders gain bone mineral density at the highest rate [ 24 , 25 ]. The cut-off age for girls is ≥ 13 years (88 participants) and for boys it is ≥ 14 years (136 participants).…”

Section: Methodsmentioning

confidence: 99%

Probabilistic prediction of segmental body composition in Iranian children and adolescents

et al. 2022

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Background Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods. Objectives To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements. Methods A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9–18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy. Results Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001). Conclusions The results revealed that, based on the present study’s predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing.

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Lean mass reference curves in adolescents using dual-energy x-ray absorptiometry (DXA)

Cited by 12 publications

References 32 publications

Less physical activity and more varied and disrupted sleep is associated with a less favorable metabolic profile in adolescents

Less physical activity and more varied and disrupted sleep is associated with a less favorable metabolic profile in adolescents

Serum polyunsaturated fatty acids in infancy are associated with body composition in adolescence

Probabilistic prediction of segmental body composition in Iranian children and adolescents

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